The proliferation of sensors and global positioning systems in mobile devices, in particular, has lead to even larger amounts of data being generated and communicated at every slice of time. Collection of such data, at times, may be helpful or needed for understanding or analysis of the environment in which the data was gathered. Since this data has multi-dimensional attributes involving time and space, the visual presentation of the data in a form that is readily comprehendible is very challenging.
For example, in the communications industry, an analyst may want to visually explore wireless hotspots that are distributed in a city, with a focus on the number of connections (i.e., occupancy) at every hour of the day for every hotspot. It may also be desirable to compare hotspots by their occupancy or correlate occupancy patterns based on the spatial distribution of the hotspots in the city. To accomplish the above tasks, the spatial topology (e.g., a map) of the city as well as an understanding of the distribution of the data produced by the sensors in the mobile or wireless network are needed.
Referring to FIG. 1A, a classic representation for the above noted data may include one or more distribution charts overlaid on a map. Unfortunately, such a solution is space inefficient in that it does not provide for a meaningful display of data on the screen when a plurality of distribution charts need to be displayed side-by-side for the purpose of comparison (e.g., comparing the size of the bars). In other words, the limitations associated with the size of the chart in relation to the map and the number of locations on the screen that are to be simultaneously presented would make it difficult for a human operator to easily assess the temporal data distributions in the spatial arrangement shown in FIG. 1A.